Bridging Academia and Accountancy Practice: Insights from Saudi Southern Region Academics
Bibliographic record
Abstract
Enhancing the relationship between academia and professional accounting bodies is critical for developing professional accountants and making them ready for real-world challenges. Not only that, it will also reflect positively on the advancement of the accounting profession. This study explores the interrelationship and the extent of collaboration between accounting education and the professional accounting body in the Kingdom of Saudi Arabia, particularly focusing on the Southern region of the kingdom and aiming to provide recommendations that will contribute to the development of the accounting profession. To achieve this objective, a quantitative design was employed, using a questionnaire and covering accounting academics. The data analysis was performed using the Statistical Package for the Social Sciences (SPSS). The findings indicated that of all participants believe in the importance of collaboration between academic institutions and the professional accounting body in Saudi. In addition, the findings reveal that almost of the participants believe there is a significant gap between academic curricula and the needs of professional practice. This gap is attributed to factors such as outdated accounting curricula, limited integration of practical skills, and insufficient collaboration with the Saudi Organization for Chartered and Professional Accountants (SOCPA). The study recommends enhancing academic curricula, increasing practical training opportunities, and fostering collaboration between universities and SOCPA to better align academic programs with professional requirements.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".